Infilling methods for monthly precipitation records with poor station network density in Subtropical Argentina

Hurtado, Santiago I.; Zaninelli, Pablo G.; Agosta, Eduardo A.; Ricetti, Lorenzo

doi:10.1016/j.atmosres.2021.105482

Cited by 20 publications

(17 citation statements)

References 65 publications

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“…The unique topography leads to complex changes in atmospheric movements and distinct climatic characteristics [43]. However, previous research has indicated that regardless of the interpolators type, the performance of an interpolation method depends on the sample density, climatological characteristics and topography, hence there is no existing consensus about the best performance interpolation method for a local region [33]. Since the analysis of spatial events has been based on specific areas [35], our results might not be generalizable to other regions.…”

Section: Conclusion and Discussionmentioning

confidence: 77%

“…For example, meteorological station 15 has the largest estimation error, followed by meteorological station 18. The estimation anomaly for a certain spatial location might be attributed to the complex weather variability [38] caused by the large elevation differences [45] in Chongqing, which could affect the performance of interpolation method [33].…”

Section: The Error and Correlation Analysismentioning

confidence: 99%

“…EBK acts as a recent development to build valid spatial models by automatically searching model parameters through a process of subsetting and simulations based on the Bayesian standpoint, which has been applied to the spatial interpolation of annual and monthly precipitation with favorable results [23,26,29]. Previous studies indicated that a more precise prediction might be the technique of geostatistical methods than deterministic interpolation [26,[29][30][31][32], whereas some studies have reached different conclusions [33]. Since none of the interpolation methods can be accurate in different conditions and regions, each method is applied by the specific hydrological conditions in the study region [34].…”

Section: Introductionmentioning

confidence: 99%

“…Analysis of spatial events has normally been region-specific [35]. Hence, specific local studies are necessary to determine the most indicated interpolation method since generalization may not be plausible [33].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Comparison of the Performance of Different Interpolation Methods in Replicating Rainfall Magnitudes under Different Climatic Conditions in Chongqing Province (China)

Yang

Bing

2021

Atmosphere

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Precipitation is considered a crucial component in the hydrological cycle and changes in its spatial pattern directly influence the water resources. We compare different interpolation techniques in predicting the spatial distribution pattern of precipitation in Chongqing. Six interpolation methods, i.e., Inverse Distance Weighting (IDW), Radial Basis Function (RBF), Diffusion Interpolation with Barrier (DIB), Kernel Interpolation with Barrier (KIB), Ordinary Kriging (OK) and Empirical Bayesian Kriging (EBK), were applied to estimate different rainfall patterns. Annual mean, rainy season and dry-season precipitation was calculated from the daily precipitation time series of 34 meteorological stations with a time span of 1991 to 2019, based on Leave-One-Out Cross-Validation (LOOCV), Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Symmetric Mean Absolute Percentage Error (SMAPE) and Nash–Sutcliffe Efficiency coefficient (NSE) as validation indexes of the applied models for calculating the error degree and accuracy. Correlation test and Spearman coefficient was performed on the estimated and observed values. A method combining Entropy Weight and Technique for Order Preference by Similarity to Ideal Solution (Entropy-Weighted TOPSIS) was introduced to rank the performance of six interpolation methods. The results indicate that interpolation technique performs better in estimating during periods of low precipitation (i.e., dry season, relative to rainy season and mean annual). The performance priorities of the six methods under the combined multiple precipitation distribution patterns are KIB > EBK > OK > RBF > DIB > IDW. Among them, KIB method has the highest accuracy which maps more accurate precipitation surfaces, with the disadvantage that estimation error is prone to outliers. EBK method is the second highest, and IDW method has the lowest accuracy with a high degree of error. This paper provides information for the application of interpolation methods in estimating rainfall spatial pattern and for water resource management of concerned regions.

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Section: Conclusion and Discussionmentioning

confidence: 77%

Section: The Error and Correlation Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Comparison of the Performance of Different Interpolation Methods in Replicating Rainfall Magnitudes under Different Climatic Conditions in Chongqing Province (China)

Yang

Bing

2021

Atmosphere

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show abstract

“…In the last two decades, several authors have attempted to identify the most reliable and accurate method of spatial rainfall interpolation in different geographical areas and spatial domains (continental, national, regional, local) [9][10][11][14][15][16]. Referring to the spatial interpolation of the rainfall phenomenon, we have to consider that the most reliable interpolator may vary depending on the different regions in which it is applied [3,17].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Different Spatial Interpolation Methods Applied to Monthly Rainfall as Support for Landscape Management

et al. 2021

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Landscape management requires spatially interpolated data, whose outcomes are strictly related to models and geostatistical parameters adopted. This paper aimed to implement and compare different spatial interpolation algorithms, both geostatistical and deterministic, of rainfall data in New Zealand. The spatial interpolation techniques used to produce finer-scale monthly rainfall maps were inverse distance weighting (IDW), ordinary kriging (OK), kriging with external drift (KED), and ordinary cokriging (COK). Their performance was assessed by the cross-validation and visual examination of the produced maps. The results of the cross-validation clearly evidenced the usefulness of kriging in the spatial interpolation of rainfall data, with geostatistical methods outperforming IDW. Results from the application of different algorithms provided some insights in terms of strengths and weaknesses and the applicability of the deterministic and geostatistical methods to monthly rainfall. Based on the RMSE values, the KED showed the highest values only in April, whereas COK was the most accurate interpolator for the other 11 months. By contrast, considering the MAE, the KED showed the highest values in April, May, June and July, while the highest values have been detected for the COK in the other months. According to these results, COK has been identified as the best method for interpolating rainfall distribution in New Zealand for almost all months. Moreover, the cross-validation highlights how the COK was the interpolator with the best least bias and scatter in the cross-validation test, with the smallest errors.

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Extreme precipitation events in the Austral Chaco region of Argentina

Lovino

Müller

Pierrestegui

et al. 2022

Intl Journal of Climatology

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This study assesses the intensity, spatial distribution and temporal variability of extreme precipitation events (EPEs) on different time scales relevant to agriculture and water resources during the 1901–2016 period over Argentina's Austral Chaco (ACh). EPEs were identified using a nonparametric approach for the Standardized Precipitation Index (SPI). The leading modes of SPI's variability were detected using the Singular Spectrum Analysis. We also compile and control the quality of precipitation observations in a scarcely monitored region. The limited number of stations conditions the interpolation of the GPCC v2018 database here used, mainly during the early twentieth century. The EPEs characterized by SPI at a 24‐month time scale, which favour long‐lasting water excesses or deficits with hydrological impacts, underwent two differentiated long‐term periods: a dry one before 1960 and a wet one from 1960 to 2005. Consistently, seasonal wet EPEs were more frequent during the wet period while seasonal droughts were more frequent and with larger spatial extent during the dry period. Seasonal EPEs, represented by the SPI at 3‐month time scale, can impact agriculture during the crops' critical growth period. Since 2006, the long‐term wetting trend has been reversed. If this behaviour continues into the coming decades, more droughts and less wetness might be expected. The EPEs present a large interannual variability with 6.3‐ and 9‐year significant cycles that, combined with long‐term trends, favoured extremely wet/dry EPEs. Most of the wet/dry EPEs occurred during El Niño/La Niña events. Although this relationship is not straightforward, we found a discriminant ENSO signal between October of the year when an ENSO event starts and February of the next year. Finally, severely dry/wet EPEs tend to have large spatial extent in the ACh region. The central‐eastern ACh region experiences the most extreme wet and dry conditions, which makes this area more prone to extreme EPEs.

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Infilling methods for monthly precipitation records with poor station network density in Subtropical Argentina

Cited by 20 publications

References 65 publications

A Comparison of the Performance of Different Interpolation Methods in Replicating Rainfall Magnitudes under Different Climatic Conditions in Chongqing Province (China)

A Comparison of the Performance of Different Interpolation Methods in Replicating Rainfall Magnitudes under Different Climatic Conditions in Chongqing Province (China)

Comparative Analysis of Different Spatial Interpolation Methods Applied to Monthly Rainfall as Support for Landscape Management

Extreme precipitation events in the Austral Chaco region of Argentina

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